ABSTRACT Breast cancer is one of the most widespread kinds of cancer, especially in women, and it has a high mortality rate. With the help of technology, it is possible to develop a computer‐aided method for the diagnosis of breast cancer, which is crucial for effective treatment. Recent breast cancer diagnosis studies utilizing numerous machine learning models were efficient and innovative. However, it has been observed that they may have problems such as long training times and low accuracy rates. To this end, in this study, we present a new classifier that utilizes a hybrid of the clonal selection algorithm (CSA) and the particle swarm optimization (PSO) algorithm for the training of the logistic regression (LR) model, which is named CSA‐PSO‐LR. The proposed method is evaluated using two publicly accessible breast cancer datasets, that is, the Wisconsin Diagnostic Breast Cancer (WDBC) database and the Wisconsin Breast Cancer Database (WBCD), with 10‐fold cross‐validation and Bayesian hyperparameter optimization techniques. Additionally, a CPU parallelization method is applied, which substantially shortens the training time of the model. The efficacy of the CSA‐PSO‐LR classifier is compared with state‐of‐the‐art machine learning algorithms and related studies in the literature. Performance analysis indicates that the proposed method achieves 98.75% accuracy and 98.27% F1‐score on the WDBC dataset, and 97.94% accuracy and 97.35% F1‐score on the WBCD dataset. These results demonstrate the potential of the proposed method as an effective approach for improving breast cancer diagnosis.

ABSTRACT Metaheuristics are advanced problem‐solving techniques that develop efficient algorithms to address complex challenges, while neural networks are algorithms inspired by the structure and function of the human brain. Combining these approaches enables the resolution of complex optimization problems that traditional methods struggle to solve. This study presents a novel approach integrating the ABC algorithm with ANNs for weight optimization. The method is further enhanced by vectorization and parallelization techniques on both CPU and GPU to improve computational efficiency. Additionally, this study introduces a cost‐sensitive fitness function tailored for multi‐class classification to optimize results by considering relationships between target class levels. It validates these advancements in two critical applications: network intrusion detection and earthquake damage estimation. Notably, this study makes a significant contribution to earthquake damage assessment by leveraging machine learning algorithms and metaheuristics to enhance predictive models and decision‐making in disaster response. By addressing the dynamic nature of earthquake damage, this research fills a critical gap in existing models and broadens the understanding of how machine learning and metaheuristics can improve disaster response strategies. In both domains, the ABC‐ANN implementation yields promising results, particularly in earthquake damage estimation, where the cost‐sensitive approach demonstrates satisfactory outcomes in macro‐F1 and accuracy. The best results for macro‐F1, weighted‐F1, and overall accuracy provides best results with the UNSW‐NB15 and earthquake datasets, showing values of 64%, 72%, 68%, and 60%, 80%, and 79%, respectively. Comparative performance evaluations reveal that the proposed parallel ABC‐ANN model, incorporating the novel cost‐sensitive fitness function and enhanced by vectorization and parallelization techniques, significantly reduces training time and outperforms state‐of‐the‐art methods in terms of macro‐F1 and accuracy in both network intrusion detection and earthquake damage estimation.

Assigning appropriate referees to a journal or conference paper is a vital task for many reasons, including enhancing the journal venue quality and reliance, fair judgement of the papers, and among many others. While assigning the referees to the papers, the editors of a journal venue need to find suitable referees who are both related to field of the given paper and have no conflict of interest with the authors of the paper. Editorial-wise this referee assignment process is implemented in a hand-crafted manner, i.e., the editor needs to find the most suitable referees to the paper via a search engine and manually refines the all unrelated and having conflict of interest authors to the paper authors. Clearly, such a manual referee searching process is tedious and time consuming for the editors. In this paper, we present an alternate automated approach for assigning referees problem using intrinsic random walk with restart proximity measure. In our experiments based on a comprehensive DBLP networks, we show that our approach, called OFFER, significantly outperforms state-of-the-art the random walk with restart based method.

Recently, underwater acoustic sensor networks (UASNs) have been proposed to explore underwater environments for scientific, commercial, and military purposes. However, long propagation delays, high transmission losses, packet drops, and limited bandwidth in underwater propagation environments make realization of reliable and energy-efficient communication a challenging task for UASNs. To prolong the lifetime of battery-limited UASNs, two critical factors (i.e., packet size and transmission power) play vital roles. At one hand, larger packets are vulnerable to packet errors, while smaller packets are more resilient to such errors. In general, using smaller packets to avoid bit errors might be a good option. However, when small packets are used, more frames should be transmitted due to the packet fragmentation, and hence, network overhead and energy consumption increases. On the other hand, increasing transmission power reduces frame errors, but this would result in unnecessary energy consumption in the network. To this end, the packet size and transmission power should be jointly considered to improve the network lifetime. In this study, an optimization framework via an integer linear programming (ILP) has been proposed to maximize the network lifetime by joint optimization of the transmission power and packet size. In addition, a realistic link-layer energy consumption model is designed by employing the physical layer characteristics of UASNs. Extensive numerical analysis through the optimization model has been also performed to investigate the tradeoffs caused by the transmission power and packet size quantitatively.

According to the World Health Organization (WHO), 31% of the world's total deaths in 2016 (17.9 million) was due to cardiovascular diseases (CVD). With the development of information technologies, it has become possible to predict whether people have heart diseases or not by checking certain physical and biochemical values at a lower cost. In this study, we have evalated a set of different classification algorithms, linear discriminant analysis and proposed a new hybrid feature selection methodology for the diagnosis of coronary heart diseases (CHD). Throughout this research effort, using three publicly available Heart Disease diagnosis datasets (UCI Machine Learning Repository), we have conducted comparative performance evaluations in terms of accuracy, sensitivity, specificity, F-measure, AUC and running time.

Although Smart Grid (SG) transformation brings many advantages to electric utilities, the longstanding challenge for all them is to supply electricity at the lowest cost. In addition, currently, the electric utilities must comply with new expectations for their operations, and address new challenges such as energy efficiency regulations and guidelines, possibility of economic recessions, volatility of fuel prices, new user profiles and demands of regulators. In order to meet all these emerging economic and regulatory realities, the electric utilities operating SGs must be able to determine and meet load, implement new technologies that can effect energy sales and interact with their customers for their purchases of electricity. In this respect, load forecasting which has traditionally been done mostly at city or country level can address such issues vital to the electric utilities. In this paper, an artificial neural network based energy consumption forecasting system is proposed and the efficiency of the proposed system is shown with the results of a set of simulation studies. The proposed system can provide valuable inputs to smart grid applications.

Due to the networking expertise, services and technical support of telecommunications operators, Smart Grid (SG) operators prefer telecommunications operators for their communications needs instead of creating private networks. In this paper, the use of Next Generation Networks (NGNs) by telecommunications operators to provide services to transnational SG operators for SG applications is evaluated. NGNs are all IP networks which are packet based and use IP to transport the various types of traffic such as data, voice, video, and signalling over converged fixed and mobile networks. The main idea of transnational SG operators is simple. By creating a huge single infrastructure for energy, more than one countries and nations can be powered at once. For this, it is not needed to install very huge power plants. Simply creating a complex network of power grid connections to each participating country is enough. The results of a set of simulation studies are given to show the efficiency of the NGN-based communication infrastructure for SG applications in terms of important network performance metrics. The results show that NGN-based communication infrastructures can carry packets based on their priority levels and bandwidth allocations in order to meet the specific requirements of SG applications.

A reliable and efficient communications system is required for the robust, affordable and secure supply of power through Smart Grids (SG). Computational requirements for Smart Grid applications can be met by utilizing the Cloud Computing (CC) model. Flexible resources and services shared in network, parallel processing and omnipresent access are some features of Cloud Computing that are desirable for Smart Grid applications. Eventhough the Cloud Computing model is considered efficient for Smart Grids, it has some constraints such as security and reliability. In this paper, the Smart Grid architecture and its applications are focused on first. The Cloud Computing architecture is explained thoroughly. Then, Cloud Computing for Smart Grid applications are also introduced in terms of efficiency, security and usability. Cloud platforms’ technical and security issues are analyzed. Finally, cloud service based existing Smart Grid projects and open research issues are presented.

The eight papers in this special section focus on industrial wireless sensor networks.

Information and communication technologies (ICT) represent a fundamental element in the growth and performance of smart grids. A sophisticated, reliable and fast communication infrastructure is, in fact, necessary for the connection among the huge amount of distributed elements, such as generators, substations, energy storage systems and users, enabling a real time exchange of data and information necessary for the management of the system and for ensuring improvements in terms of efficiency, reliability, flexibility and investment return for all those involved in a smart grid: producers, operators and customers. This paper overviews the issues related to the smart grid architecture from the perspective of potential applications and the communications requirements needed for ensuring performance, flexible operation, reliability and economics.

Electromagnetic interference, equipment noise, multi-path effects and obstructions in harsh smart grid environments make the quality-of-service (QoS) communication a challenging task for WSN-based smart grid applications. To address these challenges, a cognitive communication based cross-layer framework has been proposed. The proposed framework exploits the emerging cognitive radio technology to mitigate the noisy and congested spectrum bands, yielding reliable and high capacity links for wireless communication in smart grids. To meet the QoS requirements of diverse smart grid applications, it differentiates the traffic flows into different priority classes according to their QoS needs and maintains three dimensional service queues attributing delay, bandwidth and reliability of data. The problem is formulated as a Lyapunov drift optimization with the objective of maximizing the weighted service of the traffic flows belonging to different classes. A suboptimal distributed control algorithm (DCA) is presented to efficiently support QoS through channel control, flow control, scheduling and routing decisions. In particular, the contributions of this paper are three folds; employing dynamic spectrum access to mitigate with the channel impairments, defining multi-attribute priority classes and designing a distributed control algorithm for data delivery that maximizes the network utility under QoS constraints. Performance evaluations in ns-2 reveal that the proposed framework achieves required QoS communication in smart grid.

In this paper, we propose a cross-layer design to meet the QoS requirements for smart grids employing the cognitive radio sensor networks for their control and monitoring operations. Existing routing protocols pertaining to QoS support are not able to simultaneously handle traffic of different characteristics present in smart grids. Therefore, considering the traffic heterogeneity of smart grid applications exhibiting diverse QoS requirements, a set of priority classes is defined in order to differentiate the traffic for the respective service. Specifically, the problem is formulated as a weighted network utility maximization (WNUM) whose objective is to maximize the weighted sum of flows service. A cross-layer heuristic solution is provided to solve the utility optimization problem by performing joint routing, dynamic spectrum allocation and medium access. Performance of the proposed protocol is evaluated using ns-2, which shows that the number of flows belonging to each class are served according to their weight fraction with their respective data rate, latency and reliability requirement.

Simultaneous Localization and Mapping (SLAM) algorithms with multiple autonomous robots have received considerable attention in recent years. In general, SLAM algorithms use odometry information and measurements from exteroceptive sensors of robots. The accuracy of these measurements and the performance of the corresponding SLAM algorithm directly affect the overall success of the system. This paper presents comparative performance evaluations of three Simultaneous Localization and Mapping (SLAM) algorithms using Extended Kalman Filter (EKF), Compressed Extended Kalman Filter (CEKF) and Unscented Kalman Filter (UKF). Specifically, it focuses on their SLAM performances and processing time requirements. To show the effect of CPU power on the processing time of SLAM algorithms, two notebooks and a netbook with different specifications have been used. Comparative simulation results show that processing time requirements are consistent with the computational complexities of SLAM algorithms. The results we obtained are consistent with the CPU power tests of independent organizations and show that higher processing power decreases processing time accordingly. The results also show that CEKF is more suitable for outdoor SLAM applications where there are a lot of natural and artificial features.

Wireless networked mobile robots pursuing a common objective are envisioned to be an efficient solution for many military, industrial, commercial, and environmental applications. This paper presents the design considerations of an autonomous intruder detection system based on wireless networked robots. Specifically, three critical aspects of these systems, such as coordination and task allocation, communication, and map-based intruder detection, have been investigated and a multi-sensor fusion mechanism has been presented. Performance results show that the fusion of redundant information from different sensors can increase the accuracy of a system and reduce overall uncertainty in intruder detection applications.

Electric power grid contains three main subsystems, i.e., power generation, power transmission & distribution, and customer facilities. Recently, wireless sensor networks (WSNs) have been considered as a promising technology that can enhance all these three subsystems, making WSNs an important component of the smart grid. However, environmental noise and interference from nonlinear electric power equipments and fading in harsh smart grid environments, makes reliable communication a challenging task for single-channel WSNs for smart grid applications. To improve network capacity in smart grid environments, multi-channel WSNs might be the preferred solution while achieving simultaneous transmissions through multiple channels. In this paper, the performance of multi-channel WSNs is investigated for different spectrum environments of smart power grid, e.g., 500kV outdoor substation, main power control room and underground network transformer vaults. In addition, we also introduce potential applications of multi-channel WSNs along with the related technical challenges. Here, our goal is to envision potential advantages and applications of multi-channel WSNs for smart grid and motivate the research community to further explore this promising research area.

For 100 years, there has been no change in the basic structure of the electrical power grid. Experiences have shown that the hierarchical, centrally controlled grid of the 20th Century is ill-suited to the needs of the 21st Century. To address the challenges of the existing power grid, the new concept of smart grid has emerged. The smart grid can be considered as a modern electric power grid infrastructure for enhanced efficiency and reliability through automated control, high-power converters, modern communications infrastructure, sensing and metering technologies, and modern energy management techniques based on the optimization of demand, energy and network availability, and so on. While current power systems are based on a solid information and communication infrastructure, the new smart grid needs a different and much more complex one, as its dimension is much larger. This paper addresses critical issues on smart grid technologies primarily in terms of information and communication technology (ICT) issues and opportunities. The main objective of this paper is to provide a contemporary look at the current state of the art in smart grid communications as well as to discuss the still-open research issues in this field. It is expected that this paper will provide a better understanding of the technologies, potential advantages and research challenges of the smart grid and provoke interest among the research community to further explore this promising research area.

Wireless meshing has been envisioned as the economically viable networking paradigm to build up broadband and large-scale wireless commodity networks. Several different mesh network architectures have been conceived by both industry and academia; however many issues on the deployment of efficient and fair transport protocols are still open. In this paper, an adaptive and responsive transport protocol (AR- TP) is proposed for WMNs in order to fairly allocate the network resources among multiple flows, while minimizing the performance overhead. Compared to the classical end-to-end rate control mechanisms, an hop-by-hop congestion control approach is designed to keep track of dynamic multi-hop network characteristics in a responsive manner. In addition, a coarse-grained end-to-end reliability algorithm is integrated with the proposed hop-by-hop congestion control mechanism to provide packet level reliability at the transport layer. Performance evaluation via extensive simulation experiments show that the AR-TP protocol achieves high performance in terms of network throughput and fairness.

This work presents a resource-aware and link quality based (RLQ) routing metric to address energy limitations, link quality variations, and node heterogeneities in wireless sensor and actor networks (WSANs). The RLQ metric is a combined link cost metric, which is based on both energy efficiency and link quality statistics. The primary objective of the proposed metric is to adapt to varying wireless channel conditions, while exploiting the heterogeneous capabilities in WSANs. Different from most of the existing simulation based studies, this research effort is guided by extensive field experiments of link quality dynamics at various locations over a long period of time using recent sensor platforms, which realistically addresses the real-world wireless communication challenges in WSANs. Performance evaluations, via test-bed experiments, show that the RLQ routing metric achieves high performance in terms of packet reception rate, network throughput and network lifetime.

In this paper, coordination and communication problems in wireless sensor and actor networks (WSANs) are jointly addressed in a unifying framework. A sensor-actor coordination model is proposed based on an event-driven partitioning paradigm. Sensors are partitioned into different sets, and each set is constituted by a data-delivery tree associated with a different actor. The optimal solution for the partitioning strategy is determined by mathematical programming, and a distributed solution is proposed. In addition, a new model for the actor-actor coordination problem is introduced. The actor coordination is formulated as a task assignment optimization problem for a class of coordination problems in which the area to be acted upon needs to be optimally split among different actors. An auction-based distributed solution of the problem is also presented. Performance evaluation shows how global network objectives, such as compliance with real-time constraints and minimum energy consumption, can be achieved in the proposed framework with simple interactions between sensors and actors that are suitable for large-scale networks of energy-constrained devices.

The lifetime of a wireless sensor network (WSN) is generally limited by the battery lifetime of the sensor nodes. In this respect, efficient monitoring of the entire network's available energy is of great importance to take appropriate preventive actions. However, the physical limitations of WSNs, such as limited memory and energy resources, mandate such a monitoring mechanism to have low complexity and minimum energy dissipation. In this paper, a forecasting-based monitoring and tomography (FMT) framework is presented for WSNs. The objective of the FMT framework is to achieve overall monitoring and to capture the tomography of the available energy in WSNs with minimum energy expenditure. To reduce the amount of energy consumed for monitoring purposes, the FMT framework incorporates available energy forecasting and network aggregation mechanisms. Comparative performance evaluations show that the FMT framework achieves accurate energy monitoring and obtains the network energy tomography of large scale WSNs with minimum energy consumption.

This paper deals with IP traffic engineering (TE) for multipath selection in MPLS networks. A centralized and a distributed routing algorithms are proposed, which aggregate IP flows entering the MPLS domain, and optimally partition them among virtual flows that are forwarded on multiple paths according to their quality of service (QoS) requirements. The virtual-flow multipath routing problem is formulated as a multicommodity network flow (MCNF) problem, and is solved by implementing on-line the Dantzig-Wolfe decomposition method, which is proven to converge to the optimal solution through an iterative procedure that divides the complex optimization problem into a tractable subproblem. The proposed multipath algorithms are shown to outperform single-path routing solutions by means of extensive simulation experiments.

Wireless Sensor and Actor Networks (WSANs) are composed of a large number of heterogeneous nodes called sensors and actors. The collaborative operation of sensors enables the distributed sensing of a physical phenomenon, while the role of actors is to collect and process sensor data and perform appropriate actions.In this paper, a coordination framework for WSANs is addressed. A new sensor-actor coordination model is proposed, based on an event-driven clustering paradigm in which cluster formation is triggered by an event so that clusters are created on-the-fly to optimally react to the event itself and provide the required reliability with minimum energy expenditure. The optimal solution is determined by mathematical programming and a distributed solution is also proposed. In addition, a new model for actor-actor coordination is introduced for a class of coordination problems in which the area to be acted upon is optimally split among different actors. An auction-based distributed solution of the problem is also presented.Performance evaluation shows how global network objectives, such as compliance with real-time constraints and minimum energy consumption, can be reached in the proposed framework with simple interactions between sensors and actors that are suitable for large-scale networks of energy-constrained devices.